Construction Of Prognosis Risk Model With Ferroptosis Genes Of Acute Myeloid Leukemia Based On Bioinformatics

Objective:The aim of this study was to explore potential therapeutic targets that may be associated with ferroptosis of AML,and to analyze the possible pathways and molecular mechanisms of these AML target genes.A survival prognostic model for predicting AML survival was established based on ferroptosis related differential survival genes,and the model was verified and reliable.The prognostic model score was used for grouping to analyze the possible pathways and molecular mechanisms of survival differences in AML patients.Methods:Date from the cancer genome atlas(TCGA)database to express the spectral data of AML patients,download and finishing of the 200 patients with clinical information of AML RNA-seq data and survival data,the follow-up information and survival time is greater than the group of 173 patients with AML tumors,28 days from genome expression database(GTEx)download and select 70 cases of normal bone marrow samples as normal control group.Gene database(Ferr DB)extracted from ferroptosis death related genes,259 ferroptosis related genes in tumor group and normal group,and use limma R package comparing tumor group and normal group differences in gene selection,will ferroptosis related genes and difference intersection as a AML iron genetic variations associated with death,will get the difference of gene is associated with survival data,using survive R ferroptosis related genes linked to the prognosis of bag filter.The obtained genes were analyzed by protein interaction network to obtain the protein interaction network map,and the correlation network was constructed for gene correlation analysis.The prognostic genes were used to construct a risk prognosis model.According to the risk prognosis model,173 samples from the TCGA database were divided into high risk group and low risk group according to the median risk score.The risk curve and ROC curve were constructed for the high-risk group and the low-risk group to analyze the accuracy of the model.The GSE71014 data set from the GEO database was used to verify the accuracy of the risk prognosis model.According to the median score of the risk prognostic model,the TCGA group and the GEO group were divided into the high-risk group and the low-risk group,respectively.Functional enrichment analysis was conducted to compare the different genes in the two groups to study the possible signaling pathways between the two groups and their biological significance.Results:Use from the cancer genome atlas(TCGA)database to express the spectral data of AML patients,downloaded and compiled as model group,173 cases of AML from genome expression database(GTEx)download and select 70 cases of normal bone marrow samples as normal control group,104 cases from the GEO database Chinese leukemia samples as a validation set.A total of 259 ferroptosis related genes were extracted from the ferroptosis gene database(Ferr DB)as the target genes.Differential genes were screened out by comparing the target genes of the model group and the normal group,and prognostic genes were screened out by associating the survival data of the model group,and survival genes were obtained by intersecting the list of differential genes with the list of prognostic genes.Analyze the possible molecular mechanisms and find the core genes.The risk prognostic model was constructed,and its reliability was verified in the validation group.The tumor group and validation group were divided into low-risk groups.Functional enrichment analysis was performed on the differentially expressed genes between the two groups at high and low risk to analyze the signaling pathways and molecular mechanisms that may lead to the survival differences in AML patients in this model.Conclusion:Twenty-eight ferroptosis-related survival differential genes were identified,which may be ferroptosis-related therapeutic targets in AML.The occurrence of ferroptosis in AML may be accompanied by the presence of autophagy.A risk prognostic model based on the 15 ferroptosis-related genes(ACSF2,G6 PD,PIK3CA,SLC38A1,ATG3,SOCS1,MYB,CHAC1,ACVR1 b,DNAJB6,NCF2,BNIP3,SLC7A11,DDIT4,and SESN2)can effectively predict the overall survival rate of patients.Immune response may play an important role in the survival differences distinguished by this model.